This is not a statistical help site, and your questions appear to be about statistics, not programming in R. I would suggest that you get local statistical help, but you might try posting on a stats.stackexchange.com for remote help.
-- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sun, Apr 23, 2017 at 6:53 AM, Uri Blasbalg <uriblasb...@gmail.com> wrote: > hi all, > I'll begin with my two question and all the related information > (description of the research and the data and full output) will follow. > > 1. When i execute model1 (glmm with random intercept only for subjects): > predictor (suppBin) and outcome (DtlsBinUp) and pre-intervention variables, > it results with significance . when I carry out model 2: add the mediator > (rlctDown) too as a predictor, the association shown in the model1 isn't > significant anymore (suppBin-DtlsBinup), and for the mediator and outcome > it is (rlctDown-dtlsBinup), with higher coefficient. that should imply for > full mediation, meaning there isn't direct effect between the predictor and > the outcome, only indirect. but when i the test mediation model (monte > carlo method), I gel significant effect for total effect, direct effect and > the indirect effect. how can it be that the monte carlo contradicts what > shown when substracting model1 from model2? what am i missing? > > 2.i am having trouble in interpreting the values of the effects estimations > in the monte carlo test. I understood the coefficients for the glmm > as log odds that after transforming using exponential function can be > understood as odds and may also be expressed as probabilities. but > the estimates in the monte carlo output are much lower than those in the > glmm output. so how should they be understood. > > following are description and output, > thank you > uri. > > > > > > ********** predictor - outcome > > > Generalized linear mixed model fit by maximum likelihood (Laplace > Approximation) ['glmerMod'] > Family: binomial ( logit ) > Formula: dtlsBinUp ~ suppBin * qu + ageS + gender + (1 | PD) > Data: hypoTest > Control: glmerControl(tolPwrss = 0.001) > > AIC BIC logLik deviance df.resid > 15351.9 15406.1 -7669.0 15337.9 17111 > > Scaled residuals: > Min 1Q Median 3Q Max > -0.6655 -0.5281 -0.5140 -0.1889 5.4472 > > Random effects: > Groups Name Variance Std.Dev. > PD (Intercept) 0 0 > Number of obs: 17118, groups: PD, 200 > > Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -3.20574 0.14668 -21.856 < 2e-16 *** > suppBin 0.57468 0.15930 3.607 0.000309 *** > qu 2.02646 0.10902 18.588 < 2e-16 *** > ageS -0.09564 0.09923 -0.964 0.335151 > gender -0.05598 0.04141 -1.352 0.176458 > suppBin:qu -0.15165 0.17283 -0.877 0.380250 > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Correlation of Fixed Effects: > (Intr) suppBn qu ageS gender > suppBin -0.495 > qu -0.718 0.655 > ageS -0.673 0.010 0.002 > gender -0.179 0.008 0.034 0.065 > suppBin:qu 0.456 -0.922 -0.631 -0.004 -0.028 > > > > ********** predictor, mediator - outcome > > > Generalized linear mixed model fit by maximum likelihood (Laplace > Approximation) ['glmerMod'] > Family: binomial ( logit ) > Formula: dtlsBinUp ~ suppBin * qu + rlctDown + ageS + gender + (1 | PD) > Data: hypoTest > Control: glmerControl(tolPwrss = 0.001) > > AIC BIC logLik deviance df.resid > 14114.1 14176.0 -7049.0 14098.1 17110 > > Scaled residuals: > Min 1Q Median 3Q Max > -1.5239 -0.4638 -0.4552 -0.1487 6.8990 > > Random effects: > Groups Name Variance Std.Dev. > PD (Intercept) 0 0 > Number of obs: 17118, groups: PD, 200 > > Fixed effects: > Estimate Std. Error z value Pr(>|z|) > (Intercept) -3.69635 0.15247 -24.24 <2e-16 *** > suppBin 0.14896 0.16475 0.90 0.366 > qu 2.26040 0.11289 20.02 <2e-16 *** > rlctDown 2.06709 0.05947 34.76 <2e-16 *** > ageS -0.10680 0.10432 -1.02 0.306 > gender -0.02293 0.04360 -0.53 0.599 > suppBin:qu 0.13720 0.17963 0.76 0.445 > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Correlation of Fixed Effects: > (Intr) suppBn qu rlctDw ageS gender > suppBin -0.462 > qu -0.708 0.629 > rlctDown -0.159 -0.088 0.143 > ageS -0.665 0.000 -0.018 -0.005 > gender -0.184 0.008 0.035 0.024 0.066 > suppBin:qu 0.426 -0.916 -0.607 0.062 0.005 -0.029 > > > > > ********** predictor, mediator - outcome (function "mediate" from packege > "mediation" > > ** script (syntax): > med.out.8.1.2.1 <- mediate(model3.1, model8.1.2.med, treat = "suppBin", > mediator = "rlctDown", > sims = 1000) > > > Causal Mediation Analysis > > Quasi-Bayesian Confidence Intervals > > Mediator Groups: PD > > Outcome Groups: PD > > Output Based on Overall Averages Across Groups > > Estimate 95% CI Lower 95% CI Upper p-value > ACME (control) 0.0401 0.0321 0.0481 0 > ACME (treated) 0.0420 0.0338 0.0506 0 > ADE (control) 0.0376 0.0178 0.0575 0 > ADE (treated) 0.0395 0.0189 0.0595 0 > Total Effect 0.0796 0.0580 0.1013 0 > Prop. Mediated (control) 0.5015 0.3890 0.6852 0 > Prop. Mediated (treated) 0.5276 0.4127 0.7081 0 > ACME (average) 0.0410 0.0329 0.0492 0 > ADE (average) 0.0385 0.0183 0.0584 0 > Prop. Mediated (average) 0.5145 0.3999 0.6961 0 > > Sample Size Used: 17118 > > > Simulations: 1000 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
